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The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, tw'o or more acoustic states, and realistic (Le., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.

This book, which is the revised version of the 1995 text MULTITARGET-MULTISENSOR TRACKING: PRINCIPLES AND TECHNIQUES, at double the length, is the most comprehensive state of the art compilation of practical algorithms for the estimation of the states of targets in surveillance systems operating in a multitarget environment using data fusion. This problem is characterized by measurement origin uncertainty, typical for low observables. The tools for design of algorithms for the association of measurements and tracking are presented. Explicit consideration is given for measurements obtained from different sensors under realistic assumptions -lack of synchronicity and different detection and accuracy characteristics. Several real-data examples are given to illustrate the techniques discussed. The modeling accounts for target maneuvers, non-unity detection probability, false alarms, interference from other targets and the finite resolution capability of sensors. The problems of track initiation, maintenance and multisensor data fusion are considered. The optimization of certain signal processing parameters based on tracking performance is also discussed. The latest results on measurement extraction for unresolved targets, sensor management and data fusion are included.Many of these techniques have applications to state estimation when using multiple sensors in control systems, autonomous vehicle navigation, robotics and wireless communication. An extensive index is provided with all the indexed terms highlighted in the text for the convenience of the reader.

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